{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:MWCIRURIDDXJC5UD2WWUW6KOPF","short_pith_number":"pith:MWCIRURI","schema_version":"1.0","canonical_sha256":"658488d22818ee917683d5ad4b794e79479f8731400fcc6a1d4e803d35365a8d","source":{"kind":"arxiv","id":"1610.01058","version":1},"attestation_state":"computed","paper":{"title":"Approximation Algorithms for Stochastic k-TSP","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Alina Ene, Rishi Saket, Viswanath Nagarajan","submitted_at":"2016-10-04T15:57:25Z","abstract_excerpt":"We consider the stochastic $k$-TSP problem where rewards at vertices are random and the objective is to minimize the expected length of a tour that collects reward $k$. We present an adaptive $O(\\log k)$-approximation algorithm, and a non-adaptive $O(\\log^2k)$-approximation algorithm. We also show that the adaptivity gap of this problem is between $e$ and $O(\\log^2k)$."},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1610.01058","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2016-10-04T15:57:25Z","cross_cats_sorted":[],"title_canon_sha256":"12b00721bdfcf8b4f88bd70fc20b64d501443fe6213796354d009a66163c3736","abstract_canon_sha256":"607f0f656d298f703546b2b19fcb06527ed47eb6f1d0f3b9631492c046a57c6f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:03:12.516528Z","signature_b64":"U82BOOB8k7CN09GfAvMmNMCiJRh5vv2XCXyz0L4pyqwH1PnmEWVKpGoapZq9Ro2Jr+KGffN+K4GdFRv2R/rhDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"658488d22818ee917683d5ad4b794e79479f8731400fcc6a1d4e803d35365a8d","last_reissued_at":"2026-05-18T01:03:12.515856Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:03:12.515856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Approximation Algorithms for Stochastic k-TSP","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Alina Ene, Rishi Saket, Viswanath Nagarajan","submitted_at":"2016-10-04T15:57:25Z","abstract_excerpt":"We consider the stochastic $k$-TSP problem where rewards at vertices are random and the objective is to minimize the expected length of a tour that collects reward $k$. We present an adaptive $O(\\log k)$-approximation algorithm, and a non-adaptive $O(\\log^2k)$-approximation algorithm. We also show that the adaptivity gap of this problem is between $e$ and $O(\\log^2k)$."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.01058","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1610.01058","created_at":"2026-05-18T01:03:12.515943+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.01058v1","created_at":"2026-05-18T01:03:12.515943+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.01058","created_at":"2026-05-18T01:03:12.515943+00:00"},{"alias_kind":"pith_short_12","alias_value":"MWCIRURIDDXJ","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_16","alias_value":"MWCIRURIDDXJC5UD","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_8","alias_value":"MWCIRURI","created_at":"2026-05-18T12:30:32.724797+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MWCIRURIDDXJC5UD2WWUW6KOPF","json":"https://pith.science/pith/MWCIRURIDDXJC5UD2WWUW6KOPF.json","graph_json":"https://pith.science/api/pith-number/MWCIRURIDDXJC5UD2WWUW6KOPF/graph.json","events_json":"https://pith.science/api/pith-number/MWCIRURIDDXJC5UD2WWUW6KOPF/events.json","paper":"https://pith.science/paper/MWCIRURI"},"agent_actions":{"view_html":"https://pith.science/pith/MWCIRURIDDXJC5UD2WWUW6KOPF","download_json":"https://pith.science/pith/MWCIRURIDDXJC5UD2WWUW6KOPF.json","view_paper":"https://pith.science/paper/MWCIRURI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.01058&json=true","fetch_graph":"https://pith.science/api/pith-number/MWCIRURIDDXJC5UD2WWUW6KOPF/graph.json","fetch_events":"https://pith.science/api/pith-number/MWCIRURIDDXJC5UD2WWUW6KOPF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MWCIRURIDDXJC5UD2WWUW6KOPF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MWCIRURIDDXJC5UD2WWUW6KOPF/action/storage_attestation","attest_author":"https://pith.science/pith/MWCIRURIDDXJC5UD2WWUW6KOPF/action/author_attestation","sign_citation":"https://pith.science/pith/MWCIRURIDDXJC5UD2WWUW6KOPF/action/citation_signature","submit_replication":"https://pith.science/pith/MWCIRURIDDXJC5UD2WWUW6KOPF/action/replication_record"}},"created_at":"2026-05-18T01:03:12.515943+00:00","updated_at":"2026-05-18T01:03:12.515943+00:00"}